Automated resolution of chromatographic signals by independent component analysis–orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics. Issue 130 (July 2016)

• Record Type: Journal Article
• Authors: Domingo-Almenara, Xavier; Perera, Alexandre; Ramírez, Noelia; Brezmes, Jesus
• Is Part Of: Computer methods and programs in biomedicine. Issue 130(2016)
• Page Start: 135
• Page End: 141
• Publication Date: 2016-07
• Subjects: Comprehensive gas chromatography -- Orthogonal signal deconvolution -- Multivariate curve resolution -- Compound deconvolution -- Independent component analysis; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.03.007
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : Highlights: GC×GC–MS automated compound identification and quantification in metabolomics. Orthogonal signal deconvolution (OSD) for spectra deconvolution. PCA used as an alternative to the traditional least squares approach. OSD R package used for fast and automated compound identification. Abstract: Comprehensive gas chromatography–mass spectrometry (GC×GC–MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GC×GC–MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis–orthogonal signal deconvolution (ICA–OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GC×GC–MS chromatograms in an automated manner. We studied the performance of ICA–OSD by the quantification of 38 metabolites through a set of 20 Jurkat cell samples analyzed by GC×GC–MS. The quantification by ICA–OSD was compared with a supervised quantification by selective ions, and most of the R 2 coefficients of determination were in good agreement ( R 2 >0.90) while up to 24 cases exhibited an excellent linear relation ( R 2 >0.95). We concluded that ICA–OSD can be used to resolve co-eluted compounds in GC×GC–MS.